[talks] 1-2pm Wed May 7 talk on Learning Structured Bayesian Networks by Marie desJardins
Jennifer Rexford
jrex at CS.Princeton.EDU
Sun May 4 19:19:50 EDT 2008
Speaker: Professor Marie desJardins, University of Maryland
Title: *Learning Structured Bayesian Networks: Combining Abstraction
Hierarchies and Tree-Structured Conditional Probability Tables
Date/time: 1-2pm Wednesday May 7
Location: 302 in the CS Building
Abstract:
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In this talk, I will describe our research on incorporating
background knowledge in the form of feature hierarchies during
Bayesian network learning. Feature hierarchies enable the learning
system to aggregate categorical variables in meaningful ways, thus
enabling an appropriate "discretization" for a categorical variable.
In addition, by choosing the appropriate level of abstraction for
the parent of a node, we also support compact representations for
the local probability models in the network. We combine this notion
of selecting an appropriate abstraction with context-specific
independence representations, which capture local ndependence
relationships among the random variables in the Bayesian network.
Capturing this local structure is important because it reduces the
number of parameters required to represent the distribution. This
can lead to more robust parameter estimation and structure
selection, more efficient inference algorithms, and more
interpretable models.
I will describe our primary contribution, the Tree-Abstraction-Based
Search (TABS) algorithm, which learns a data distribution by
inducing the graph structure and parameters of a Bayesian network
from training data. TABS combines tree structure and attribute-value
hierarchies to compactly represent conditional probability tables.
In order to construct the attribute-value hierarchies, we
investigate two data-driven techniques: a global clustering method,
which uses all of the training data to build the attribute-value
hierarchies, and can be performed as a preprocessing step; and a
local clustering method, which uses only the local network structure
to learn attribute-value hierarchies. Empirical results in several
benchmark domains show that (1) combining tree structure and
attribute-value hierarchies improves the accuracy of generalization,
while providing a significant reduction in the number of parameters
in the learned networks, and (2) data-derived hierarchies perform as
well or better than expert-provided hierarchies.
BIOGRAPHY Dr. Marie desJardins is an associate professor in the
Department of Computer Science and Electrical Engineering at the
University of Maryland, Baltimore County. Her research is in
artificial intelligence, focusing on the areas of machine learning,
multi-agent systems, planning, interactive AI techniques,
information management, reasoning with uncertainty, and decision
theory.
Dr. desJardins can be contacted at the Dept. of Computer Science and
Electrical Engineering, University of Maryland Baltimore County,
1000 Hilltop Circle, Baltimore MD 21250, mariedj at cs.umbc.edu,(410)
455-3967.
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